final-fnd.ipynb

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    "import pandas as pd\n",
    "import sklearn\n",
    "import itertools\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import re\n",
    "import nltk\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords"
   ]
  },
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    "train_df = pd.read_csv(r'C:\\Users\\Mayur\\Downloads\\train.csv')"
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       "    id                                              title  \\\n",
       "0    0  House Dem Aide: We Didn’t Even See Comey’s Let...   \n",
       "1    1  FLYNN: Hillary Clinton, Big Woman on Campus - ...   \n",
       "2    2                  Why the Truth Might Get You Fired   \n",
       "3    3  15 Civilians Killed In Single US Airstrike Hav...   \n",
       "4    4  Iranian woman jailed for fictional unpublished...   \n",
       "5    5  Jackie Mason: Hollywood Would Love Trump if He...   \n",
       "6    6  Life: Life Of Luxury: Elton John’s 6 Favorite ...   \n",
       "7    7  Benoît Hamon Wins French Socialist Party’s Pre...   \n",
       "8    8  Excerpts From a Draft Script for Donald Trump’...   \n",
       "9    9  A Back-Channel Plan for Ukraine and Russia, Co...   \n",
       "10  10  Obama’s Organizing for Action Partners with So...   \n",
       "11  11  BBC Comedy Sketch \"Real Housewives of ISIS\" Ca...   \n",
       "12  12  Russian Researchers Discover Secret Nazi Milit...   \n",
       "13  13  US Officials See No Link Between Trump and Russia   \n",
       "14  14  Re: Yes, There Are Paid Government Trolls On S...   \n",
       "\n",
       "                          author  \\\n",
       "0                  Darrell Lucus   \n",
       "1                Daniel J. Flynn   \n",
       "2             Consortiumnews.com   \n",
       "3                Jessica Purkiss   \n",
       "4                 Howard Portnoy   \n",
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       "6                            NaN   \n",
       "7                Alissa J. Rubin   \n",
       "8                            NaN   \n",
       "9   Megan Twohey and Scott Shane   \n",
       "10                   Aaron Klein   \n",
       "11               Chris Tomlinson   \n",
       "12                 Amando Flavio   \n",
       "13                    Jason Ditz   \n",
       "14                  AnotherAnnie   \n",
       "\n",
       "                                                 text  label  \n",
       "0   House Dem Aide: We Didn’t Even See Comey’s Let...      1  \n",
       "1   Ever get the feeling your life circles the rou...      0  \n",
       "2   Why the Truth Might Get You Fired October 29, ...      1  \n",
       "3   Videos 15 Civilians Killed In Single US Airstr...      1  \n",
       "4   Print \\nAn Iranian woman has been sentenced to...      1  \n",
       "5   In these trying times, Jackie Mason is the Voi...      0  \n",
       "6   Ever wonder how Britain’s most iconic pop pian...      1  \n",
       "7   PARIS  —   France chose an idealistic, traditi...      0  \n",
       "8   Donald J. Trump is scheduled to make a highly ...      0  \n",
       "9   A week before Michael T. Flynn resigned as nat...      0  \n",
       "10  Organizing for Action, the activist group that...      0  \n",
       "11  The BBC produced spoof on the “Real Housewives...      0  \n",
       "12  The mystery surrounding The Third Reich and Na...      1  \n",
       "13  Clinton Campaign Demands FBI Affirm Trump's Ru...      1  \n",
       "14  Yes, There Are Paid Government Trolls On Socia...      1  "
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    "train_df = train_df.drop(\"author\", axis = 1)\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_distribution(dataFile):\n",
    "    return sb.countplot(x='label', data=dataFile, palette='hls')\n",
    "\n",
    "# by calling below we can see that training, test and valid data seems to be failry evenly distributed between the classes\n",
    "create_distribution(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T06:50:32.306146Z",
     "iopub.status.busy": "2021-05-25T06:50:32.305826Z",
     "iopub.status.idle": "2021-05-25T06:50:32.335357Z",
     "shell.execute_reply": "2021-05-25T06:50:32.33417Z",
     "shell.execute_reply.started": "2021-05-25T06:50:32.306118Z"
    }
   },
   "outputs": [],
   "source": [
    "def data_qualityCheck():\n",
    "    print(\"Checking data qualitites...\")\n",
    "    train_df.isnull().sum()\n",
    "    train_df.info()  \n",
    "    print(\"check finished.\")\n",
    "data_qualityCheck()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T06:50:32.337061Z",
     "iopub.status.busy": "2021-05-25T06:50:32.336735Z",
     "iopub.status.idle": "2021-05-25T06:50:32.367948Z",
     "shell.execute_reply": "2021-05-25T06:50:32.366933Z",
     "shell.execute_reply.started": "2021-05-25T06:50:32.33703Z"
    }
   },
   "outputs": [],
   "source": [
    "train_df = train_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T06:50:32.369967Z",
     "iopub.status.busy": "2021-05-25T06:50:32.369528Z",
     "iopub.status.idle": "2021-05-25T06:50:32.399296Z",
     "shell.execute_reply": "2021-05-25T06:50:32.398264Z",
     "shell.execute_reply.started": "2021-05-25T06:50:32.369924Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data qualitites...\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20761 entries, 0 to 20799\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   text    20761 non-null  object\n",
      " 1   label   20761 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 486.6+ KB\n",
      "check finished.\n"
     ]
    }
   ],
   "source": [
    "data_qualityCheck()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T06:50:32.401314Z",
     "iopub.status.busy": "2021-05-25T06:50:32.400868Z",
     "iopub.status.idle": "2021-05-25T06:50:32.407806Z",
     "shell.execute_reply": "2021-05-25T06:50:32.406589Z",
     "shell.execute_reply.started": "2021-05-25T06:50:32.401272Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20761, 2)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T06:50:32.409912Z",
     "iopub.status.busy": "2021-05-25T06:50:32.409162Z",
     "iopub.status.idle": "2021-05-25T06:50:32.426843Z",
     "shell.execute_reply": "2021-05-25T06:50:32.425727Z",
     "shell.execute_reply.started": "2021-05-25T06:50:32.409868Z"
    }
   },
   "outputs": [
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    "train_df.head(10)"
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  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T06:50:32.42892Z",
     "iopub.status.busy": "2021-05-25T06:50:32.42853Z",
     "iopub.status.idle": "2021-05-25T06:50:32.433555Z",
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     "shell.execute_reply.started": "2021-05-25T06:50:32.428889Z"
    }
   },
   "outputs": [],
   "source": [
    "train_df.reset_index(drop= True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T06:50:32.435265Z",
     "iopub.status.busy": "2021-05-25T06:50:32.434781Z",
     "iopub.status.idle": "2021-05-25T06:50:32.454591Z",
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    }
   },
   "outputs": [
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   "source": [
    "label_train = train_df.label"
   ]
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  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T06:50:32.46513Z",
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       "Name: label, dtype: int64"
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     "execution_count": 68,
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   "source": [
    "train_df = train_df.drop(\"label\", axis = 1)"
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    {
     "data": {
      "text/plain": [
       "'Comments Actor Steven Seagal has just stood up for America, while the rest of Hollywood remains silent. This week has been rough for our country. First, Democratic nominee Hillary Clinton collapsed at the 9/11 memorial. Then, she called millions of hardworking Americans “deplorable.” As if that weren’t enough, NFL players throughout the country have been blatantly disrespecting the American flag. Needless to say, Seagal had enough. “I think the most important job as Secretary of State is ensuring our people don’t get killed,” Seagal tweeted. “If you can’t do [email protected] “Pneumonia or not, she is going to be disastrous for the American people. #NoToHillary,” he continued. Of course, Seagal quickly became the target of liberal fire for his comments, but he refused to break down. He particularly lost it when one Twitter user tried to argued that Hillary was the most capable for the presidency. “Capable? Capable of leaving AMERICANS to die. Capable of disregarding law. Capable of disrespecting rape survivors,” he argued. He then went on to address race relations in the United States, and the true role President Barack Obama has played in the social evolution of this country. “Obama has been abysmal for race relations in USA. #TRUTH We need to start having honest dialog,” he wrote. Seagal concluded by pointing out the irony of the attacks he was receiving from liberals everywhere. “Best thing in the world…Making one statement about freedom and getting attacked by every Demo out there… #HYPOCRITICAL,” he tweeted. “An America without a democrat in the White House, is a SAFER America.” What do you think of Seagal’s comments? '"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   },
   "outputs": [],
   "source": [
    "lemmatizer = WordNetLemmatizer()\n",
    "stpwrds = list(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
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   },
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   "source": [
    "for x in range(len(train_df)) :\n",
    "    corpus = []\n",
    "    review = train_df['text'][x]\n",
    "    review = re.sub(r'[^a-zA-Z\\s]', '', review)\n",
    "    review = review.lower()\n",
    "    review = nltk.word_tokenize(review)\n",
    "    for y in review :\n",
    "        if y not in stpwrds :\n",
    "            corpus.append(lemmatizer.lemmatize(y))\n",
    "    review = ' '.join(corpus)\n",
    "    train_df['text'][x] = review      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T07:14:51.798724Z",
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    {
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       "'comment actor steven seagal stood america rest hollywood remains silent week rough country first democratic nominee hillary clinton collapsed memorial called million hardworking american deplorable werent enough nfl player throughout country blatantly disrespecting american flag needle say seagal enough think important job secretary state ensuring people dont get killed seagal tweeted cant email protected pneumonia going disastrous american people notohillary continued course seagal quickly became target liberal fire comment refused break particularly lost one twitter user tried argued hillary capable presidency capable capable leaving american die capable disregarding law capable disrespecting rape survivor argued went address race relation united state true role president barack obama played social evolution country obama abysmal race relation usa truth need start honest dialog wrote seagal concluded pointing irony attack receiving liberal everywhere best thing worldmaking one statement freedom getting attacked every demo hypocritical tweeted america without democrat white house safer america think seagals comment'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "train_df['text'][2182]"
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    "execution": {
     "iopub.execute_input": "2021-05-25T07:16:37.152728Z",
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   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(train_df['text'], label_train, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
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       "(14532,)"
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       "10140    1\n",
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     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T07:18:10.901469Z",
     "iopub.status.busy": "2021-05-25T07:18:10.901136Z",
     "iopub.status.idle": "2021-05-25T07:18:22.003384Z",
     "shell.execute_reply": "2021-05-25T07:18:22.002314Z",
     "shell.execute_reply.started": "2021-05-25T07:18:10.90144Z"
    }
   },
   "outputs": [],
   "source": [
    "tfidf_v = TfidfVectorizer()\n",
    "tfidf_X_train = tfidf_v.fit_transform(X_train)\n",
    "tfidf_X_test = tfidf_v.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T07:18:24.321674Z",
     "iopub.status.busy": "2021-05-25T07:18:24.321329Z",
     "iopub.status.idle": "2021-05-25T07:18:24.327063Z",
     "shell.execute_reply": "2021-05-25T07:18:24.325975Z",
     "shell.execute_reply.started": "2021-05-25T07:18:24.321644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14532, 137427)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T07:18:31.418929Z",
     "iopub.status.busy": "2021-05-25T07:18:31.418573Z",
     "iopub.status.idle": "2021-05-25T07:18:31.427535Z",
     "shell.execute_reply": "2021-05-25T07:18:31.426865Z",
     "shell.execute_reply.started": "2021-05-25T07:18:31.418889Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T07:18:36.942583Z",
     "iopub.status.busy": "2021-05-25T07:18:36.942Z",
     "iopub.status.idle": "2021-05-25T07:18:37.23373Z",
     "shell.execute_reply": "2021-05-25T07:18:37.233Z",
     "shell.execute_reply.started": "2021-05-25T07:18:36.942549Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassiveAggressiveClassifier()"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = PassiveAggressiveClassifier()\n",
    "classifier.fit(tfidf_X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T07:18:41.422338Z",
     "iopub.status.busy": "2021-05-25T07:18:41.421887Z",
     "iopub.status.idle": "2021-05-25T07:18:41.673492Z",
     "shell.execute_reply": "2021-05-25T07:18:41.672498Z",
     "shell.execute_reply.started": "2021-05-25T07:18:41.422308Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.54%\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_pred = classifier.predict(tfidf_X_test)\n",
    "score = metrics.accuracy_score(Y_test, Y_pred)\n",
    "print(f'Accuracy: {round(score*100,2)}%')\n",
    "cm = metrics.confusion_matrix(Y_test, Y_pred)\n",
    "plot_confusion_matrix(cm, classes=['FAKE Data', 'REAL Data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T07:47:25.718988Z",
     "iopub.status.busy": "2021-05-25T07:47:25.718614Z",
     "iopub.status.idle": "2021-05-25T07:47:25.723756Z",
     "shell.execute_reply": "2021-05-25T07:47:25.722952Z",
     "shell.execute_reply.started": "2021-05-25T07:47:25.718959Z"
    }
   },
   "outputs": [],
   "source": [
    "pickle.dump(classifier,open('./model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T07:47:30.821844Z",
     "iopub.status.busy": "2021-05-25T07:47:30.821333Z",
     "iopub.status.idle": "2021-05-25T07:47:30.825935Z",
     "shell.execute_reply": "2021-05-25T07:47:30.824983Z",
     "shell.execute_reply.started": "2021-05-25T07:47:30.821811Z"
    }
   },
   "outputs": [],
   "source": [
    "# load the model from disk\n",
    "loaded_model = pickle.load(open('./model.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-25T08:03:34.889218Z",
     "iopub.status.busy": "2021-05-25T08:03:34.888860Z",
     "iopub.status.idle": "2021-05-25T08:03:34.895792Z",
     "shell.execute_reply": "2021-05-25T08:03:34.894703Z",
     "shell.execute_reply.started": "2021-05-25T08:03:34.889189Z"
    }
   },
   "outputs": [],
   "source": [
    "def fake_news_det(news):\n",
    "    review = news\n",
    "    review = re.sub(r'[^a-zA-Z\\s]', '', review)\n",
    "    review = review.lower()\n",
    "    review = nltk.word_tokenize(review)\n",
    "    for y in review :\n",
    "        if y not in stpwrds :\n",
    "            corpus.append(lemmatizer.lemmatize(y))     \n",
    "    input_data = [' '.join(corpus)]\n",
    "    vectorized_input_data = tfidf_v.transform(input_data)\n",
    "    prediction = loaded_model.predict(vectorized_input_data)\n",
    "    if prediction[0] == 0:\n",
    "        print(\"Prediction of the News :  Looking Fake⚠ News📰 \")\n",
    "    else:\n",
    "        print(\"Prediction of the News : Looking Real News📰 \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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